MACHINE LEARNING

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Solicitudes publicadas en los últimos 30 días / Applications published in the last 30 days



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RESERVOIR MODELING AND WELL PLACEMENT USING MACHINE LEARNING

Publication No.: WO2022170358A1 11/08/2022

Applicant:

SCHLUMBERGER TECHNOLOGY CORP [US]
SCHLUMBERGER CA LTD [CA]
SERVICES PETROLIERS SCHLUMBERGER [FR]
GEOQUEST SYSTEMS BV [NL]

Absstract of: WO2022170358A1

A method includes receiving data representing reservoir properties for a subsurface volume, conducting an uncertainty analysis by simulating different model realizations representing the subsurface volume, identifying a first hot spot of the subsurface volume based on the uncertainty analysis, the first hot spot representing an area having a high predicted performance, relative to other areas of the subsurface volume, based on the simulating of the different model realizations representing the subsurface volume, identifying a second hot spot of the subsurface volume using a machine learning model that is trained to predict well performance based on the one or more reservoir properties, evaluating the first and second hot spots for well placement based on the predicted well performance at the first and second hot spots, respectively, and selecting at least one of the first hot spot or the second hot spot for well construction.

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SECURE STORAGE AND PROCESSING OF DATA FOR GENERATING TRAINING DATA

Publication No.: WO2022169584A1 11/08/2022

Applicant:

MICROSOFT TECHNOLOGY LICENSING LLC [US]

US_2022253540_A1

Absstract of: WO2022169584A1

Techniques for securely storing and processing data for training data generation are provided. In one technique, multiple encrypted records are retrieved from a first persistent storage. For each encrypted record, that record is decrypted in memory to generate a decrypted record that comprises multiple attribute values. Then, based on the attribute values and a definition of multiple features of a machine-learned model, multiple feature values are generated and stored, along with a label, in a training instance, which is then stored in a second persistent storage. One or more machine learning techniques are used to train the machine-learned model based on training data that includes the training instances that are stored in the second persistent storage.

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ASSESSING PROJECT QUALITY USING CONFIDENCE ANALYSIS OF PROJECT COMMUNICATIONS

Publication No.: US2022253787A1 11/08/2022

Applicant:

IBM [US]

Absstract of: US2022253787A1

An embodiment trains a machine-learning model using a first training corpus of general items indicative of varying levels of confidence. The embodiment also prepares a second training corpus that includes domain-specific items indicative of varying levels of confidence extracted from communications from members of a project group associated with a project. The embodiment retrains the machine-learning model using the second training corpus and generates a confidence score for the project based on confidence values assigned by the machine-learning model to each of a plurality of project-related communication items from members of the project group. The embodiment also detects that the confidence score is below a predetermined threshold confidence level and, in response, initiates a communication to members of the project group conveying information regarding an automated remedial action for the project.

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DEVICES AND METHODS FOR LATTICE POINTS ENUMERATION

Publication No.: US2022253670A1 11/08/2022

Applicant:

INST MINES TELECOM [FR]

KR_20220027155_PA

Absstract of: US2022253670A1

A lattice prediction device for predicting a number of lattice points falling inside a bounded region in a given vector space is provided. The bounded region is defined by a radius value, a lattice point representing a digital signal in a lattice constructed over the vector space. The lattice is defined by a lattice generator matrix comprising components. The lattice prediction device comprises a computation unit configured to determine a predicted number of lattice points by applying a machine learning algorithm to input data derived from the radius value and the components of lattice generator matrix.

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SYSTEM FOR SECURE OBFUSCATION OF ELECTRONIC DATA WITH DATA FORMAT PRESERVATION

Publication No.: US2022253544A1 11/08/2022

Applicant:

BANK OF AMERICA [US]

Absstract of: US2022253544A1

Embodiments of the invention are directed to systems, methods, and computer program products for utilizing machine learning to identify data which is to be obfuscated in a format-preserving manner, which allows the obfuscated or masked data to appear as though it is original data. Because this type of obfuscation technique may require a higher degree of computational power than other techniques, there is a need to be able to dynamically choose when to implement format preservation based on a variety of factors. By using machine learning techniques, the present invention provides the functional benefit of analyzing both the data to be obfuscated, as well as available computational resources, to determine when it is appropriate to apply a format-preserving masking algorithm to the data. Accordingly, the present invention may ensure that organizational data is appropriately masked while preventing the resource strain associated with preserving the format of all original data.

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SECURE STORAGE AND PROCESSING OF DATA FOR GENERATING TRAINING DATA

Publication No.: US2022253540A1 11/08/2022

Applicant:

MICROSOFT TECHNOLOGY LICENSING LLC [US]

WO_2022169584_A1

Absstract of: US2022253540A1

Techniques for securely storing and processing data for training data generation are provided. In one technique, multiple encrypted records are retrieved from a first persistent storage. For each encrypted record, that record is decrypted in memory to generate a decrypted record that comprises multiple attribute values. Then, based on the attribute values and a definition of multiple features of a machine-learned model, multiple feature values are generated and stored, along with a label, in a training instance, which is then stored in a second persistent storage. One or more machine learning techniques are used to train the machine-learned model based on training data that includes the training instances that are stored in the second persistent storage.

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ELECTRONIC SYSTEM FOR IDENTIFYING FAULTY CODE AND VULNERABILITIES IN SOFTWARE PROGRAMS USING LINKED EVALUATION TOOLS

Publication No.: US2022253532A1 11/08/2022

Applicant:

BANK OF AMERICA [US]

Absstract of: US2022253532A1

Systems, computer program products, and methods are described herein for dynamically generating linked security tests. The present invention may be configured to perform security tests on an application, generate, based on the results of the security tests, security test sequences that include at least one security test that the application failed, perform the security test sequences on the application, and, iteratively and until the application passes each security test sequence in an iteration, generate additional security test sequences. The present invention may be further configured to provide results of the security tests and security test sequences to one or more machine learning models to generate supplementary security test sequences and determine probabilities of the application failing the supplementary security test sequences.

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MACHINE-LEARNING BASED PERSONALIZATION

Publication No.: US2022253496A1 11/08/2022

Applicant:

SITECORE CORP A/S [DK]

US_2020272672_A1

Absstract of: US2022253496A1

A system, method, and apparatus provide the ability to generate and deliver personalized digital content. Multiple content tests are performed by presenting different variants of content to a set of different consumers of one or more consumers. A machine learning (ML model is generated and trained based on an analysis of results of the multiple content tests. Based on the ML model, personalization rules, that specify a certain variance for a defined set of facts, are output. The personalization rules are exposed to an administrative user who selects one or more of the personalization rules. A request for content is received from a requesting consumer. Based on similarities between the defined set of facts and the requesting consumer, a subset of the selected personalization rules are selected. The content is personalized and delivered to the requesting consumer based on the further selected personalization rules.

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ENHANCED SENSOR OPERATION

Publication No.: US2022256123A1 11/08/2022

Applicant:

FORD GLOBAL TECH LLC [US]
UNIV MICHIGAN STATE [US]

DE_102022102443_PA

Absstract of: US2022256123A1

A two-dimensional image of a vehicle occupant in a vehicle is collected. The collected two-dimensional image is input to a machine learning program trained to output one or more reference points of the vehicle occupant, each reference point being a landmark of the vehicle occupant. One or more reference points of the vehicle occupant in the two-dimensional image is output from the machine learning program. A location of the vehicle occupant in an interior of the vehicle is determined based on the one or more reference points. A vehicle component is actuated based on the determined location. For each of the one or more reference points, a similarity measure is determined between the reference point and a three-dimensional reference point, the similarity measure based on a distance between the reference point and the three-dimensional reference point.

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THIN DATA WARNING AND REMEDIATION SYSTEM

Publication No.: US2022255881A1 11/08/2022

Applicant:

TRIANGLE IP INC [US]

US_11323388_B1

Absstract of: US2022255881A1

The present disclosure describes a patent management system and method for remediating insufficiency of input data for a machine learning system. A plurality of data vectors using data are extracted from a plurality of data sources. A user input with respect to an input data context is received, the input data context correspond to a subset of the plurality of data elements. An input vector based on the user input is generated and a set of matching data vectors are determined from the plurality of data vectors based on the input vector. An insufficiency of the input data is determined based on a comparison of a number of matching data vectors with a first pre-determined threshold, and/or a variance with a second pre-determined threshold. Further, the set of matching data vectors are expanded by modifying the input vector when the input data is determined to be insufficient.

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Method and System for Determining and Reclassifying Valuable Words

Publication No.: US2022253728A1 11/08/2022

Applicant:

AWOO INTELLIGENCE INC [TW]

Absstract of: US2022253728A1

Method and system for determining and reclassifying valuable words, wherein a large amount of text and valuable words are pre-inputted into a word processing server for machine learning. Moreover, the word processing server is trained on the valuable words and many labels associated with the valuable words such that it can learn and determines the valuable words in the text that meet the definition of the valuable word. The valuable word is further extracted from the text and re-classified after extraction. In addition, each valuable word is provided with various relevance labels to facilitate the subsequent application of the valuable words.

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MACHINE LEARNING MODEL FOR ENTITY RESOLUTION

Publication No.: US2022253725A1 11/08/2022

Applicant:

CAPITAL ONE SERVICES LLC [US]

Absstract of: US2022253725A1

In some implementations, a system may define common attributes of a first dataset and a second dataset. The system may generate a candidate set of mappings between one or more entities in the first dataset and one or more entities in the second dataset based on candidate generation criteria associated with a related pair of common attributes. The system may generate feature sets for the candidate set of mappings based on the common attributes and a featurization configuration. The system may train a machine learning model for performing entity resolution between the first dataset and the second dataset. The system may perform entity resolution between the first dataset and the second dataset based on the feature sets for the candidate set of mappings using the trained machine learning model.

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AMPLIFYING SOURCE CODE SIGNALS FOR MACHINE LEARNING

Publication No.: US2022253723A1 11/08/2022

Applicant:

IBM [US]

Absstract of: US2022253723A1

Embodiments are disclosed for a method. The method includes identifying one or more source code signals in a source code. The method also include generating an amplified code based on the identified signals and the source code. The amplified code is functionally equivalent to the source code. Further, the amplified code includes one or more amplified signals. The method additionally includes providing the amplified code for a machine learning model that is trained to perform a source code relevant task.

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AUTOMATED RECOMMENDATIONS FOR TASK AUTOMATION

Publication No.: US2022253790A1 11/08/2022

Applicant:

WORKFUSION INC [US]

US_2021042684_A1

Absstract of: US2022253790A1

In one embodiment, a method for providing recommendations for workflow alteration is disclosed. Task results for completion of a first set of iterations of a workflow are received. Training data may be extracted from the task results. The training data may be used to build a machine learning model for altering at least a portion of the workflow. An automation forecast that assesses the effects of altering the workflow for a second set of the iterations of the task may be generated, and a workflow alteration recommendation may be provided. Based on automation parameters, such as a minimum required level of accuracy, and the automation forecast, a recommendation regarding whether to automate the task may be included in the workflow alteration recommendation. Finally, based on the recommendation, an automated process may be generated to handle at least a portion of the task.

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HYDROCARBON OIL FRACTION PREDICTION WHILE DRILLING

Publication No.: US2022253726A1 11/08/2022

Applicant:

SAUDI ARABIAN OIL CO [SA]

Absstract of: US2022253726A1

A method includes building a mud-gas hydrocarbon oil fraction database comprising historical data, training a machine learning model using the historical data in the mud-gas hydrocarbon oil fraction database, drilling a new wellbore, processing drilling mud returns, from the new wellbore, through a gas sampler comprising a gas chromatograph and a gas mass spectrometer, retrieving real-time mud-gas data from the gas sampler, and generating a real-time hydrocarbon oil fraction log for the new wellbore by processing the real-time mud-gas data through the trained machine learning model and producing estimated hydrocarbon oil fraction data.

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SCHEMA AUGMENTATION SYSTEM FOR EXPLORATORY RESEARCH

Publication No.: US2022253719A1 11/08/2022

Applicant:

MICROSOFT TECHNOLOGY LICENSING LLC [US]

WO_2022169719_A1

Absstract of: US2022253719A1

In examples, a schema augmentation system for exploratory research leverages intelligence from a machine learning model to augment such tasks by leveraging intelligence derived from machine learning capabilities. Augmenting tasks include schematization of content, such as information units and groupings of information units. Based on the schematization of such content, semantic proximities for information units are determined. The semantic proximities may be used to identify and present potentially relevant information units, for example to accelerate the exploratory research task at hand. As such, users engaged in consumption of heterogeneous content (e.g., across client applications and/or content sources), may receive machine-augmented support to find potential information units. To optimize machine training, user input may be received, such that the system may intelligently augment the user's exploratory research task based on the semantic coherence of the content processed from information units and associated user behavior.

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A MACHINE LEARNING APPROACH TO MULTI-DOMAIN PROCESS AUTOMATION AND USER FEEDBACK INTEGRATION

Publication No.: WO2022167904A1 11/08/2022

Applicant:

SISCALE AI INC [US]

US_2022245511_A1

Absstract of: WO2022167904A1

Embodiments relate to multi-domain process automation with user feedback integration. Some embodiments include a method performed by one or more computing devices. The one or more computing devices generate, using a machine learning (ML) model, predictions for records. The one or more computing devices receive at least one of single user feedback or multiple user feedback for the predictions. The one or more computing devices generate a user validated record pool based on the single user feedback or multiple user feedback. The one or more computing devices update the ML model using the user validated record pool.

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USAGE RESTRICTIONS FOR DIGITAL CERTIFICATES

Publication No.: US2022247575A1 04/08/2022

Applicant:

IBM [US]

Absstract of: US2022247575A1

A method, a computer program product, and a system for usage restrictions on digital certificates. The method includes selecting a digital certificate relating to a user and determining a usage restriction policy for the digital certificate based on the user. The method also includes populating an extension field of the digital certificate with the usage restriction policy. The method further includes providing the digital certificate including the usage restriction policy to the user. The method also includes gathering parameters relating to the digital certificate, determining usage patterns based on the parameters, inputting the usage patterns into a machine learning model, outputting a risk assessment, and updating the usage restriction policy based on the risk assessment.

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DEVICES AND METHODS FOR MACHINE LEARNING ASSISTED SPHERE DECODING

Publication No.: US2022247605A1 04/08/2022

Applicant:

INST MINES TELECOM [FR]

KR_20220027966_PA

Absstract of: US2022247605A1

A decoder for decoding a signal received through a transmission channel represented by a channel matrix using a search sphere radius. The decoder comprises a radius determination device for determining a search sphere radius from a preliminary radius. The radius determination device is configured to: i. apply a machine learning algorithm to input data derived from the received signal, the channel matrix and a current radius, the current radius being initially set to the preliminary radius, which provides a current predicted number of lattice points associated with the current radius; ii. compare the current predicted number of lattice points to a given threshold; iii. update the current radius if the current predicted number of lattice points is strictly higher than the given threshold, the current radius being updated by applying a linear function to the current radius; Steps i to iii are iterated until a termination condition is satisfied, the termination condition being related to the current predicted number, the radius determination device being configured to set the search sphere radius to the current radius in response to the termination condition being satisfied.

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SYSTEMS AND METHODS FOR GENERATING BASKET AND ITEM QUANTITY PREDICTIONS USING MACHINE LEARNING ARCHITECTURES

Publication No.: US2022245707A1 04/08/2022

Applicant:

WALMART APOLLO LLC [US]

Absstract of: US2022245707A1

Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform acts of: generating a feature vector for a user based, at least in part, on historical data pertaining to the user's previous transactions; generating, using a quantity prediction model of a machine learning architecture, a respective item quantity prediction for each of one or more items included in a predicted basket based, at least in part, on the feature vector for the user; and populating a respective quantity selection option for each of the one or more items included in the predicted basket based on the respective item quantity prediction generated for each of the one or more items. Other embodiments are disclosed herein.

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TECHNIQUES FOR ADAPTIVE QUANTIZATION LEVEL SELECTION IN FEDERATED LEARNING

Publication No.: US2022245527A1 04/08/2022

Applicant:

QUALCOMM INC [US]

WO_2022164808_A1

Absstract of: US2022245527A1

Methods, systems, and devices for wireless communications are described. To support adaptive quantization level selection in federated learning, a server may cause a base station to transmit an indication of a quantization level for a user equipment (UE) to use to compress gradient data output by a machine learning model. For example, the server may determine, for each UE of a set of UEs, a respective quantization level for respective gradient data that is output by a respective machine learning model at each UE. The server may transmit, to each UE via one or more base stations, first information for use as an input in the respective machine learning model and an indication of the respective quantization level. A UE may receive the first information and the indication and may transmit, to the server, compressed gradient data that is generated based on (e.g., using) the indicated quantization level.

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SYSTEMS AND METHODS FOR GENERATING TIME SLOT PREDICTIONS AND REPURCHASE PREDICTIONS USING MACHINE LEARNING ARCHITECTURES

Publication No.: US2022245530A1 04/08/2022

Applicant:

WALMART APOLLO LLC [US]

Absstract of: US2022245530A1

Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform functions comprising: generating one or more feature vectors for a user, the one or more feature vectors at least comprising transaction-based features and slot-based features; generating, using a machine learning architecture, a repurchase prediction for the user based, at least in part, on the one or more feature vectors; generating, using the machine learning architecture, a time slot prediction for the user based, at least in part, on the one or more feature vectors, the time slot prediction predicting a time slot desired by the user for an upcoming transaction; and executing a reservation function that facilitates reserving of the time slot for the user. Other embodiments are disclosed herein.

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DISTRIBUTED MACHINE LEARNING FOR IMPROVED PRIVACY

Publication No.: US2022245524A1 04/08/2022

Applicant:

SNAP INC [US]

US_11341429_B1

Absstract of: US2022245524A1

Methods, computer readable media, devices, and systems provide for distributed machine learning. In one aspect, a method of training a model is disclosed. The method includes receiving, by a client device, from one or more servers, an intermediate model, training, by the client device, the intermediate model based on private data, and transmitting, by the client device, to the one or more servers, the trained intermediate model.

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MACHINE LEARNING APPROACH TO MULTI-DOMAIN PROCESS AUTOMATION AND USER FEEDBACK INTEGRATION

Publication No.: US2022245511A1 04/08/2022

Applicant:

SISCALE AI INC [US]

WO_2022167904_A1

Absstract of: US2022245511A1

Embodiments relate to multi-domain process automation with user feedback integration. Some embodiments include a method performed by one or more computing devices. The one or more computing devices generate, using a machine learning (ML) model, predictions for records. The one or more computing devices receive at least one of single user feedback or multiple user feedback for the predictions. The one or more computing devices generate a user validated record pool based on the single user feedback or multiple user feedback. The one or more computing devices update the ML model using the user validated record pool.

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AUTOMATIC INTENT GENERATION WITHIN A VIRTUAL AGENT PLATFORM

Nº publicación: US2022245489A1 04/08/2022

Applicant:

SALESFORCE COM INC [US]

Absstract of: US2022245489A1

The present disclosure is directed techniques for executing a task or service using a virtual agent. A method includes: defining a plurality of intents; conducting a first tier of machine learning analysis to compare a received input string with a first subset of training phrases associated with the plurality of intents to extract one or more parameters of the received input string; conducting a second tier of machine learning analysis to compare an output of the first tier of machine learning analysis with a second subset of training phrases associated with the plurality of intents, wherein the comparison is used to generate respective similarity scores indicating whether the received input string matches one or more of the second subset of training phrases; selecting an intent from among the plurality of intents based on the respective similarity scores; and executing an action associated with the selected intent.

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